This program proposed improved adaptive genetic algorithms saccording to the idea that the best individualon current generation should bekept tonext
generation,but the best individual should becrossed and mutated by some probability.
This GUI can be used by entering nu at the MATLAB command prompt. The user can either select a function (f(x)) of their choice or a statistical distribution probability distribution function to plot over a user defined range. The function s integral can be evaluated over a user defined range by using: The composite trapezium, simpsons and gauss-legendre rules. This is useful for calculating accurate probabilities that one might see in statistical tables.
Ultra wideband (UWB) technology, well-known for its use in ground penetrating
radar, has also been of considerable interest in communications and radar applications
demanding low probability of intercept and detection (LPI/D), multipath immunity, high
data throughput, precision ranging and localization.
Ultra wideband (UWB) technology, well-known for its use in ground penetrating
radar, has also been of considerable interest in communications and radar applications
demanding low probability of intercept and detection (LPI/D), multipath immunity, high
data throughput, precision ranging and localization.
dysii is a C++ library for distributed probabilistic inference and learning in large-scale dynamical systems. It provides methods such as the Kalman, unscented Kalman, and particle filters and smoothers, as well as useful classes such as common probability distributions and stochastic processes.
The BNL toolbox is a set of Matlab functions for defining and estimating the
parameters of a Bayesian network for discrete variables in which the conditional
probability tables are specified by logistic regression models. Logistic regression can be
used to incorporate restrictions on the conditional probabilities and to account for the
effect of covariates. Nominal variables are modeled with multinomial logistic regression,
whereas the category probabilities of ordered variables are modeled through a cumulative
or adjacent-categories response function. Variables can be observed, partially observed,
or hidden.
統(tǒng)計模式識別工具箱（Statistical Pattern Recognition Toolbox）包含：
1，Analysis of linear discriminant function
2，F(xiàn)eature extraction: Linear Discriminant Analysis
3，probability distribution estimation and clustering
4，Support Vector and other Kernel Machines
Compression using lempel-ziv
-for a dictionary size of 2k
-provide dictionary
Lempel ziv algorithm is a dictionary based algorithm that addresses byte sequences from former contents instead of the original data. This algorithm consists of a rule for parsing strings of symbols from a finite alphabet into substrings, whose lengths do not exceed a prescribed integer and a coding scheme which maps these substrings sequentially into uniquely decipherable code words of fixed length. The strings are selected so that they have nearly equal probability of occurrence. Frequently-occurring symbols are grouped into longer strings while occasional symbols appear in short strings.
) Compression using huffman code
-with a number of bits k per code word
-provide huffman table
Huffman coding is optimal for a symbol-by-symbol coding with a known input probability distribution.This technique uses a variable-length code table for encoding a source symbol. The table is derived in a particular way based on the estimated probability of occurrence for each possible value of the source symbol .Huffman coding uses a specific method for representing each symbol, resulting in a prefix code that expresses the most common characters using shorter strings of bits than those used for less common source symbols.The Huffman coding is a procedure to generate a binary code tree.
